1. Loading libraries and data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder

df = pd.read_csv('customer_churn.csv')

2. Interpreting the dataset

df.head()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

df.shape

(7043, 21)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

df.isnull().sum()

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

df.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges         object
Churn                object
dtype: object

3. Data cleaning

df = df.drop(columns = ['customerID'], axis = 1)
df.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors = 'coerce')

df.isnull().sum()

gender               0
SeniorCitizen        0
Partner              0
Dependents           0
tenure               0
PhoneService         0
MultipleLines        0
InternetService      0
OnlineSecurity       0
OnlineBackup         0
DeviceProtection     0
TechSupport          0
StreamingTV          0
StreamingMovies      0
Contract             0
PaperlessBilling     0
PaymentMethod        0
MonthlyCharges       0
TotalCharges        11
Churn                0
dtype: int64

df.fillna(df['TotalCharges'].mean())

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No
7039	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No
7040	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes
7042	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No

7043 rows × 20 columns

df.drop(labels = df[df['tenure'] == 0].index, axis = 0 , inplace = True)

df[df['tenure'] == 0].index

Index([], dtype='int64')

df['SeniorCitizen'] = df['SeniorCitizen'].map({0: "No", 1: "Yes"})

df.head()

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	No	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	No	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	Male	No	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	No	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	No	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

df.describe()

	tenure	MonthlyCharges	TotalCharges
count	7032.000000	7032.000000	7032.000000
mean	32.421786	64.798208	2283.300441
std	24.545260	30.085974	2266.771362
min	1.000000	18.250000	18.800000
25%	9.000000	35.587500	401.450000
50%	29.000000	70.350000	1397.475000
75%	55.000000	89.862500	3794.737500
max	72.000000	118.750000	8684.800000

df.describe(include = 'O')

	gender	SeniorCitizen	Partner	Dependents	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	Churn
count	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032
unique	2	2	2	2	2	3	3	3	3	3	3	3	3	3	2	4	2
top	Male	No	No	No	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	No
freq	3549	5890	3639	4933	6352	3385	3096	3497	3087	3094	3472	2809	2781	3875	4168	2365	5163

df.describe(include = 'O').T

	count	unique	top	freq
gender	7032	2	Male	3549
SeniorCitizen	7032	2	No	5890
Partner	7032	2	No	3639
Dependents	7032	2	No	4933
PhoneService	7032	2	Yes	6352
MultipleLines	7032	3	No	3385
InternetService	7032	3	Fiber optic	3096
OnlineSecurity	7032	3	No	3497
OnlineBackup	7032	3	No	3087
DeviceProtection	7032	3	No	3094
TechSupport	7032	3	No	3472
StreamingTV	7032	3	No	2809
StreamingMovies	7032	3	No	2781
Contract	7032	3	Month-to-month	3875
PaperlessBilling	7032	2	Yes	4168
PaymentMethod	7032	4	Electronic check	2365
Churn	7032	2	No	5163

df.describe(include = 'all')

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
count	7032	7032	7032	7032	7032.000000	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032	7032.000000	7032.000000	7032
unique	2	2	2	2	NaN	2	3	3	3	3	3	3	3	3	3	2	4	NaN	NaN	2
top	Male	No	No	No	NaN	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	NaN	NaN	No
freq	3549	5890	3639	4933	NaN	6352	3385	3096	3497	3087	3094	3472	2809	2781	3875	4168	2365	NaN	NaN	5163
mean	NaN	NaN	NaN	NaN	32.421786	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	64.798208	2283.300441	NaN
std	NaN	NaN	NaN	NaN	24.545260	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30.085974	2266.771362	NaN
min	NaN	NaN	NaN	NaN	1.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	18.250000	18.800000	NaN
25%	NaN	NaN	NaN	NaN	9.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	35.587500	401.450000	NaN
50%	NaN	NaN	NaN	NaN	29.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	70.350000	1397.475000	NaN
75%	NaN	NaN	NaN	NaN	55.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	89.862500	3794.737500	NaN
max	NaN	NaN	NaN	NaN	72.000000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	118.750000	8684.800000	NaN

df

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	No	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	No	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	Male	No	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	No	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	No	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	No	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No
7039	Female	No	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No
7040	Female	No	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	Male	Yes	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes
7042	Male	No	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No

7032 rows × 20 columns

df1 = df
df1['Churn'].replace(to_replace = 'Yes', value = 1, inplace = True)
df1['Churn'].replace(to_replace = 'No', value = 0, inplace = True)
df_dummies = pd.get_dummies(df1)
df_dummies.head()

	tenure	MonthlyCharges	TotalCharges	Churn	gender_Female	gender_Male	SeniorCitizen_No	SeniorCitizen_Yes	Partner_No	Partner_Yes	...	StreamingMovies_Yes	Contract_Month-to-month	Contract_One year	Contract_Two year	PaperlessBilling_No	PaperlessBilling_Yes	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
0	1	29.85	29.85	0	True	False	True	False	False	True	...	False	True	False	False	False	True	False	False	True	False
1	34	56.95	1889.50	0	False	True	True	False	True	False	...	False	False	True	False	True	False	False	False	False	True
2	2	53.85	108.15	1	False	True	True	False	True	False	...	False	True	False	False	False	True	False	False	False	True
3	45	42.30	1840.75	0	False	True	True	False	True	False	...	False	False	True	False	True	False	True	False	False	False
4	2	70.70	151.65	1	True	False	True	False	True	False	...	False	True	False	False	False	True	False	False	True	False

5 rows × 47 columns

plt.figure(figsize = (15, 8))
sns.set(style = 'white')
df_dummies.corr()['Churn'].sort_values(ascending = False).plot(kind = 'bar')

<Axes: >

!pip install sweetviz

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import sweetviz as sv
my_report = sv.analyze(df)
my_report.show_html('report_html')

                                             |                                             | [  0%]   00:00 ->…

Report report_html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.

4. Visualizing the data

gender_labels = ['Male', 'Female']
churn_labels = ['No', 'Yes']

fig = make_subplots(rows = 1, cols = 2, specs = [[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels = gender_labels, values = df['gender'].value_counts(), name = 'Gender'), 1, 1)
fig.add_trace(go.Pie(labels = churn_labels, values = df['Churn'].value_counts(), name = 'Churn'), 1, 2)

fig.update_traces(hole = .55, hoverinfo = "label+percent+name", textfont_size = 16)

fig.update_layout(title_text = "Gender and Churn Distributions",
                 annotations = [dict(text = 'Gender', x=0.16, y=0.5, font_size=20, showarrow=False),
                               dict(text = 'Gender', x=0.83, y=0.5, font_size=20, showarrow=False)])

fig.data[0].marker.colors = ('#7fcdff', '#326ada')
fig.data[1].marker.colors = ('#56c175', '#ff9b35')
fig.show()

#color_discrete_map = {"Month-to-month": "#7fcdff", "One year": "#326ada", "Two year": "#ff9b355"}
fig = px.histogram(df, x = 'Churn', color = 'Contract', barmode = 'group')
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

75% of customer who have Month-to-Month Contract have opted to move out as compared to 13% of customers who have signed One Year Contract and 3% of customers who have signed Two Year Contract.

fig = px.histogram(df, x = 'Churn', color = "PaymentMethod", title = "<b>Churn distribution w.r.t Custome Payment Method</b>", text_auto = True)

fig.update_layout(width = 700, height = 500, bargap= 0.1)
fig.data[0].marker.color = ('#7fcdff')
fig.data[1].marker.color = ('#ff9b35')
fig.data[2].marker.color = ('#56c175')
fig.show()

Majority of the customers who moved out were having Electronic Check as Payment Method and others who opted for Credit-Card automatic transfer / Bank Automatic Transfer and Mailed Check as Payment Method were less likely to switch.

df[df['gender'] == "Male"][["InternetService", "Churn"]].value_counts()

InternetService  Churn
DSL              0        992
Fiber optic      0        910
No               0        717
Fiber optic      1        633
DSL              1        240
No               1         57
Name: count, dtype: int64

df[df['gender'] == "Female"][["InternetService", "Churn"]].value_counts()

InternetService  Churn
DSL              0        965
Fiber optic      0        889
No               0        690
Fiber optic      1        664
DSL              1        219
No               1         56
Name: count, dtype: int64

fig = go.Figure()

colors = {'Female': 'steelblue', 'Male': 'firebrick'}

fig.add_trace(go.Bar(
    x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
        ["Female", "Male", "Female", "Male"]],
    y = [965, 992, 219, 240],
    name = 'DSL'))
fig.add_trace(go.Bar(
    x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
        ['Female', 'Male', 'Female', 'Male']],
    y = [889, 910, 664, 663],
    name = 'Fiber optic'))
fig.add_trace(go.Bar(
    x = [['Churn:No', 'Churn:No', 'Churn:Yes', 'Churn:Yes'],
        ['Female', 'Male', 'Female', 'Male']],
    y = [690, 717, 56, 57],
    name = 'No Internet'))

fig.update_layout(title_text= "<b>Churn Distribution w.r.t Internet Service and Gender</b>")
fig.data[0].marker.color = ('#7fcdff', '#7fcdff', '#7fcdff', '#7fcdff')
fig.data[1].marker.color = ('#326ada', '#326ada', '#326ada', '#326ada')
fig.data[2].marker.color = ('#ff9b35','#ff9b35','#ff9b35','#ff9b35')
fig.show()

Fiber optic service which was choosen by a lot of customers and it's evident that there's high churn rate among these customers. This could expose an issue in the Fiber optic service which dissatisfied most of it's customers, further looking into the issue might find a better and apt solution. Customers who opted for DSL service are larger in number and is found to have less churn rate compared to Fibre optic service

fig = px.histogram(df, x = 'Churn', color = 'Dependents', barmode = 'group', title = '<b>Churn distribution w.r.t. Dependents</b>')
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

Customers without dependents are more likely to churn

color_map = {"Yes": "#7fcdff", "No": "#326ada"}
fig = px.histogram(df, x = 'Churn', color = 'Partner', barmode = "group", title = '<b>Churn distribution w.r.t. Partners</b>', color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

color_map = {"Yes": '#7fcdff', "No": '#326ada'}
fig = px.histogram(df, x = 'Churn', color = 'SeniorCitizen', title = "<b>Churn distribution w.r.t Senior Citizen</b>", color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

On the above visual, a conclusion can be obtained such that customers without dependents and customers who have partners are more likely to churn while senior citizens being the most of churn.

color_map = {"Yes": "#7fcdff", "No": "#326ada", "No internet service": "#ff9b35"}
fig = px.histogram(df, x = "Churn", color = "OnlineSecurity", barmode = "group", title = "<b>Churn w.r.t online security</b>", color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

Absence of online security,makes most customers churn.

color_map = {"Yes": "#7fcdff", "No": "#326ada"}
fig = px.histogram(df, x = "Churn", color = "PaperlessBilling", barmode = "group", title = "<b>Churn distribution w.r.t Paperless Billing</b>", color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

Paperless Billing seems like one the reasons because of which customers are most likely to churn.

color_map = {"Yes": "#7fcdff", "No": "#326ada", "No internet service": "#ff9b35"}

fig = px.histogram(df, x = 'Churn', color = 'TechSupport', barmode = 'group', title = '<b>Churn distribution w.r.t Techsupport</b>', color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

The absence of online security, Paperless Billing system and services with no TechSupport were the similiar trend are of the customers who are most likely churn.

color_map = {"Yes": '#7fcdff', "No": '#326ada'}
fig = px.histogram(df, x = "Churn", color = "PhoneService", title = "<b>Churn distribution w.r.t Phone service</b>", color_discrete_map = color_map)
fig.update_layout(width = 700, height = 500, bargap = 0.1)
fig.show()

Eventhough there's a small fraction of customers but it's better to point out as they are more likely to churn because don't have a phone service.

Conclusions as a Data Analyst : ● 75% of customer who have Month-to-Month Contract have opted to move out as compared to 13% of customers who have signed One Year Contract and 3% of customers who have signed Two Year Contract. ● Majority of the customers who moved out were having Electronic Check as Payment Method and others who opted for Credit-Card automatic transfer / Bank Automatic Transfer and Mailed Check as Payment Method were less likely to switch. ● Fiber optic service which was choosen by a lot of customers and it's evident that there's high churn rate among these customers. This could expose an issue in the Fiber optic service which dissatisfied most of it's customers, further looking into the issue might find a better and apt solution. ● Customers who opted for DSL service are larger in number and is found to have less churn rate compared to Fibre optic service ● Customers without dependents and customers who have partners are more likely to churn while senior citizens being the most of churn. ● The absence of online security, Paperless Billing system and services with no TechSupport were the similiar trend are of the customers who are most likely churn. ● There's a small fraction of customers who are more likely to churn and it's been found that they don't have a phone service. A Data Analyst's work done here. Now,comes Data Scientist who makes a model to predict the churn in the future data.